MO Genome Comparisons 14/10

Question 1

From sequencing to genome

Answer 1

1. Obtaining Raw Sequence Reads:
   The starting point for genomic analysis is obtaining raw sequence reads, typically generated from sequencing platforms like Illumina or Oxford Nanopore.
   These raw sequence files come in .fastq format, which contains both the sequence data and the corresponding quality scores.
2. Quality Control:
   Before moving forward, the raw reads must undergo quality control checks using tools like FastQC. This process identifies and removes low-quality reads, adapters, and other unwanted artifacts, ensuring high-quality data for downstream analysis.
3. Read Trimming:
   The reads are then trimmed to remove low-quality bases and sequencing adapters.
4. Genome Assembly:
   There are two main approaches to assembling genomes:
   De Novo Assembly: Used when no reference genome is available. Reads are assembled into contigs (contiguous sequences) and scaffolds (ordered contigs). Tools like SPAdes (for short reads) or Flye (for long reads) are commonly used. The assembly process can result in a genome file in .fasta format.
   Reference-Based Assembly: When a reference genome exists, the new reads are aligned to the reference genome using mappers like Bowtie2. This approach is typically faster and more accurate.
5. Genome Annotation:
   After assembly, the genome is annotated to identify genes, coding regions, and functional elements. This step involves:
   Gene Prediction: Tools like AUGUSTUS and BRAKER2 are used for gene prediction, combining ab initio methods (predicting genes based solely on genomic data) with evidence-based methods (using RNA-seq data to inform predictions).
   Functional Annotation: Once genes are identified, they are functionally annotated using databases like InterPro or Pfam to assign potential functions to the predicted genes.
   The final output is an annotated genome file containing information on genes, coding sequences, and their biological roles.
6. Quality Control of Annotation
   Tools like BUSCO (Benchmarking Universal Single-Copy Orthologs) are used to assess the completeness of the genome and ensure that essential genes are present in the assembly.

Question 2

Annotation Methods

Answer 2

annotation refers to the process of identifying and labeling gene locations and functions within a genome

Ab Initio Methods: These rely on algorithms and models to predict gene locations based on sequence features like codons and splice sites. They do not use experimental data and can generate predictions based solely on DNA sequences.

Evidence-Based Methods: These use experimental data, such as cDNA sequences or protein homology, to identify genes. They incorporate biological evidence, improving accuracy by confirming predictions against known data.

Question 3

Difference SNP’s, SNV’s and genomic variants

Answer 3

SNPs are variations in a single nucleotide that are common in a population, typically defined as occurring in at least 1% of the population.
SNVs (single nuceotide variants) refer to any single nucleotide change, regardless of how frequent or rare it is. This includes both common and rare variations, even those unique to an individual.
genomic variants
- all genomic changes, this includes larger deletions, insertions and genome re-arrangements. This also includes SNVs and is therefore the safest but least precise term

Question 4

From Genome Files to Successful Comparison of Genomes

Answer 4

1. Genome Comparison Basics:
Once the genome files are assembled and annotated, the next step is comparing genomes to identify genetic differences like single nucleotide polymorphisms (SNPs), structural variants, or large insertions and deletions.
For accurate comparisons, you need:
A reference genome (for example, a well-characterized strain or ancestor).
The query genome (the genome you’re comparing against the reference).
2. Mapping Reads to the Reference Genome:
Using a tool like Bowtie2, raw reads from the query genome are mapped to the reference genome. This process aligns the reads to the reference, allowing you to detect variants between the genomes.
The output is a .bam file containing the aligned reads.
3. Variant Calling
Once the reads are aligned, variant callers like FreeBayes or GATK HaplotypeCaller are used to identify single nucleotide variants (SNVs) and other genomic variants.
The result is a variant call format (VCF) file listing the variants found between the query and reference genomes.
4. Annotation of Variants:
To assess the functional impact of the detected variants, tools like SnpEff are used to annotate the VCF file. SnpEff classifies variants as high-impact (e.g., causing premature stop codons) or low-impact (e.g., synonymous mutations) based on their predicted effects on gene function.
These annotations help prioritize variants that may be responsible for observed phenotypes, such as drug resistance or pathogenicity.
5. Comparing Multiple Genomes:
For more complex studies involving multiple genomes, tools like MUMmer or NUCmer can be used to compare entire genomes. These tools align large regions between genomes and detect structural differences such as duplications, inversions, or rearrangements.
When comparing multiple strains, phylogenetic tools like Orthofinder (to identify orthologous genes) and RaxML (to construct phylogenetic trees based on genome-wide data) are used to visualize evolutionary relationships.
6. Visualizing Differences
Visualization tools such as Integrative Genomics Viewer (IGV) allow researchers to explore genomic data, visualize alignments, and inspect variant calls in detail. Large datasets can also be summarized with statistical plots like Manhattan plots, which show the distribution of variants across the genome.
7. Functional Insights and Hypothesis Testing:
After identifying potential variants, it is crucial to test the functional implications. For example, if a mutation is suspected of causing antibiotic resistance, experimental validation (e.g., by introducing the mutation into a wild-type strain) is necessary to confirm its causal role.